To develop a predictive model to forecast energy load demand based on historical load data.
This analysis will help utility providers (like Nstar) to balance the grid, in terms of demand and supply. This is a constant problem faced by them, due to the difficulty of storing energy.
2. AGENDA
• Objective
• Methodology
• Data Exploration : Visualization
• Data Mining
• Analysis : Multiple Linear Regression
• Analysis : Neural Networks
• Analysis : Regression Trees
• Time Series Forecasting
• Analysis : Model Based Forecasting
• Analysis : Data Based Forecasting
• Final Model Selection
• Recommendation
• Q&A
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3. To develop a predictive model to forecast energy load demand
based on historical load data.
BUSINESS APPLICATION
This analysis will help utility providers (like Nstar) to balance the
grid, in terms of demand and supply. This is a constant problem
faced by them, due to the difficulty of storing energy.
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OBJECTIVE
4. • Source: kaggle.com.
• Load History: hourly load data (in Kw) for 20 zones.
• Time period: January 1st , 2004 to June 29th 2008.
• Temperature history: hourly temperature data from 11
stations.
• Relation between load zones and temperature stations not
provided.
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DATASET
7. Create a model to predict load, with good predictive ability as
indicated by:
• Predictor variables
• Low Root Mean Squared Error (RMSE)
• Low Mean Absolute Percentage Error (MAPE)
• Low Mean Absolute Deviation (MAD)
TOOLS
• Tableau : for Visualization and Data Exploration.
• MS Excel : Data cleaning.
• XL Miner: Data preparation, Partitioning, and Predictive analysis.
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PROPOSED SOLUTION
21. USING HISTORICALDATAIN VALIDATION
• Need for accuracy in load prediction increases closer to time.
• Used actual historical values rather than forecast.
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